3DS : Cherry Blossom

Lyuda Bekwinknoll, Meghana Cyanam, Theresa Marie Duenas, Kevin Kiser

With our data visualization we are determining the association between age and fitness based on running data from the Cherry Blossom Ten-mile Run held in Washington DC from 1973 to 2020.

Loading and Cleaning Data

Describing our Data

Variable Names Data Type Variable Descriptions
Year Integer Year the race was held.
Name Character First and last name of runner.
Age Integer Age of runner at time of race. He
Time Time/Numeric Time in hr:min:sec format to run 10 miles.
Division Character Groupings based on age and gender.
pos_by_sex Integer
total_by_sex Integer
Sex Character Gender of runner.
PRCP Numeric
TMAX Integer Temperature maximum for the race day
TMIN Integer Temperature minimum for the race day

Dataset Overview:

In the original data set we have 347402 rows and 17 columns. After cleaning the data set we ended up with 339934 rows and 11 columns. 7468 rows of data were omitted from the data we used because they had missing values for the time and/or age variables. Below is the description of the variables and data we excluded for our data analysis/visualization:

What was excluded Reason for exclusion
Hometown
Distance
Date
pos_by_div
total_by_division
Pace
Year 1977?

Summary Statistics:

Year, Age, Time, Sex main variables to focus on.

Checklist for this section:

summary stats: mean, median, mode, range, sd, percentiles, distributions by sex variable, etc.

mention how many women and how many men in each year and overall

summary.data.frame(df)
##       Year          Name                Age            Time         
##  Min.   :1974   Length:339214      Min.   : 8.0   Min.   :00:43:20  
##  1st Qu.:2001   Class :character   1st Qu.:29.0   1st Qu.:01:19:35  
##  Median :2009   Mode  :character   Median :35.0   Median :01:30:50  
##  Mean   :2006                      Mean   :36.6   Mean   :01:31:25  
##  3rd Qu.:2015                      3rd Qu.:43.0   3rd Qu.:01:42:22  
##  Max.   :2019                      Max.   :87.0   Max.   :02:20:00  
##                                                                     
##    Division           pos_by_sex     total_by_sex       Sex           
##  Length:339214      Min.   :    1   Min.   :   27   Length:339214     
##  Class :character   1st Qu.: 1109   1st Qu.: 3513   Class :character  
##  Mode  :character   Median : 2445   Median : 6792   Mode  :character  
##                     Mean   : 3134   Mean   : 6298                     
##                     3rd Qu.: 4739   3rd Qu.: 9030                     
##                     Max.   :11042   Max.   :11042                     
##                     NA's   :6       NA's   :6                         
##       PRCP             TMAX           TMIN      
##  Min.   :0.0000   Min.   :44.0   Min.   :32.00  
##  1st Qu.:0.0000   1st Qu.:56.0   1st Qu.:39.00  
##  Median :0.0000   Median :64.0   Median :43.00  
##  Mean   :0.0538   Mean   :63.3   Mean   :43.11  
##  3rd Qu.:0.0500   3rd Qu.:70.0   3rd Qu.:47.00  
##  Max.   :0.9300   Max.   :84.0   Max.   :58.00  
##

These were helping me evaluate the data cleaning, we can fix or replace them later

 plot_age_dist <- ggplot(df, aes(x = Age, y = as.factor(Year))) +
    geom_density_ridges_gradient(
      aes(fill = ..x..), scale = 3, size = 0.3
    ) +
    scale_fill_gradientn(
      colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
      name = "Age"
    ) +
    labs(title = 'Age Distribution by Year', y="")
  
plot_age_dist  
## Warning: The dot-dot notation (`..x..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(x)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Picking joint bandwidth of 1.6

  # Plotting density ridgeline plot for Time by Year
 plot_time_dist <-  ggplot(df, aes(x = Time, y = as.factor(Year))) +
    geom_density_ridges_gradient(
      aes(fill = ..x..), scale = 3, size = 0.3
    ) +
    scale_fill_gradientn(
      colours = c("red", "purple", "blue"),
      name = "Time to finish"
    ) +
    labs(title = 'Time Distribution by Year', y = "")
 plot_time_dist 
## Don't know how to automatically pick scale for object of type <times>.
## Defaulting to continuous.
## Picking joint bandwidth of 0.00152